结直肠癌诊断实现结果
基于电子病历特征，应用机器学习模型进行结直肠癌诊断的数据集。
基本信息
资源简介
结直肠癌诊断实现结果
【数据集背景】
本数据集源站标题为《Implementation results.》。源站首次发布：2025-04-29。
【数据内容】
源站原始描述：In this paper, we present the results of applying machine learning models to build a Colorectal Cancer Diagnosis system. The methodology encompasses six key steps: collecting raw data from Electronic Medical Records (EMRs), revising feature attributes with expert input, data preprocessing, model adaptation, training machine learning models (CART, Random Forest, and XGBOOST), and evaluating the results. Furthermore, based on analysis of experimental measurement parameter values, 21 feature attrib…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 2.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0321009.t002。
【主题与分类】
源站学科分类：Biotechnology、Chemical Sciences not elsewhere classified、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Cancer、Science Policy。主题标签：system assists clinicians、support early diagnose、select clinical tests、colorectal cancer patient、colorectal cancer disease、applying machine learning、21 feature attributes、medical examination costs、electronic medical records、collecting raw data。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Implementation_results_/28897983
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Minh, Nguyen Hai; Quy, Tran Quang; Tam, Ngo Duc; Tuan, Tran Manh; Son, Le Hoang (2025). Implementation results.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Implementation results.
- DOI：10.1371/journal.pone.0321009.t002
- 发布日期：2025-04-29
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Implementation_results_/28897983
命令行下载
curl -L -o "Table 2.xls" "https://ndownloader.figshare.com/files/54073266"
文件清单
Table 2.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
数据说明
In this paper, we present the results of applying machine learning models to build a Colorectal Cancer Diagnosis system. The methodology encompasses six key steps: collecting raw data from Electronic Medical Records (EMRs), revising feature attributes with expert input, data preprocessing, model adaptation, training machine learning models (CART, Random Forest, and XGBOOST), and evaluating the results. Furthermore, based on analysis of experimental measurement parameter values, 21 feature attributes which relate to support early diagnose the Colorectal cancer disease are extracted. Among different models implemented in our case, XGBOOST is the most suitable model to solve this problem. The system assists clinicians to select clinical tests and medical procedures for a colorectal cancer patient. Therefore, patients can save the waiting time and medical examination costs. On the other hand, based on the achievements from this research, our approach can guide further applying machine learning in medicine.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0321009.t002（source: https://plos.figshare.com/articles/dataset/Implementation_results_/28897983）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
